MIBTY

DATA

AI & robotics

We build AI and robotics systems that ship — from custom models to retrieval, evaluation, control loops, and the surrounding infrastructure that turns intelligence into reliable software and hardware.

Book a consultationProduction AI and robotics, not demos.

The problem

Most AI projects stall between the notebook and the system. The model works; the product doesn't.

MIBTY exists for the second half: turning model behavior into infrastructure that holds up under real users, real edge cases, and real cost constraints.

Workflow

  1. 01

    Frame

    We map the decision the AI has to make, the data it can see, and the constraints — latency, cost, audit. No model selection before this.

  2. 02

    Prototype

    Smallest end-to-end system that touches every layer: retrieval, model, eval, UX. Measured against a baseline.

  3. 03

    Evaluate

    We build the eval set first. Models get swapped; the eval stays. This is what makes the system improvable.

  4. 04

    Scale

    Observability, fallbacks, caching, cost ceilings, on-call. The boring infrastructure that lets the interesting model run for years.

Benefits

  • Evaluation-first

    We build the test set before we choose the model. That's the difference between AI that improves and AI that drifts.

  • Latency you can quote

    P95 budgets enforced at the architecture level — streaming, caching, parallel calls — not hoped for at runtime.

  • Cost ceilings, not surprises

    Per-call cost tracked from day one. We design for the unit economics, not against them.

Technologies

  • Claude
  • GPT-5
  • Open-weights (Llama, Mistral)
  • vLLM
  • PyTorch
  • LangGraph
  • Weights & Biases
  • DSPy
  • Pydantic
  • Modal
  • Vercel AI SDK
  • pgvector

Industries served

  • Healthcare
  • Government
  • Enterprise
  • Research
  • Startups

Frequent questions

  • Do you train custom models or use frontier models?

    Both — and the choice is the work. Frontier models for breadth and pace, fine-tuned or distilled models where latency, cost, or privacy demand it. We don't have a religion.

  • How do you handle hallucination and reliability?

    Evaluation harnesses for every behavior we ship, deterministic fallbacks for high-stakes paths, structured outputs validated at the boundary, and audit logs by default.

  • Who owns the IP?

    You do — code, weights of fine-tunes, eval sets. We retain only generic methodology.

  • What's the engagement model?

    Fixed-scope projects (6–16 weeks), embedded teams (3–12 months), or research partnerships. We'll recommend a fit on the first call.